Automated Algorithm Selection on Continuous Black-Box Problems By Combining Exploratory Landscape Analysis and Machine Learning

LTHOUGH the Algorithm Selection Problem (ASP, [1]) has been introduced more than four decades ago, there only exist few works (e.g., [2], [3]), which perform algorithm selection in the field of continuous optimization. Independent of the underlying domain, the goal of the ASP can be described as follows: given a set of optimization algorithms A, often denoted algorithm portfolio, and a set of problem instances I, one wants to find a model m: I A that selects the best algorithm A A from the portfolio for an unseen problem instance I I. Albeit there already exists a plethora of optimization algorithms - even when only considering singleobjective, continuous optimization problems - none of them can be considered to be superior to all the other ones across all optimization problems. Hence, it is very desirable to find a sophisticated selection mechanism, which automatically picks the portfolio's best solver for a given problem. Within other optimization domains, such as the well-known Travelling Salesperson Problem, feature-based algorithm selectors have already shown their capability of outperforming the respective state-of-the-art optimization algorithm(s) by combining machine learning techniques and problem dependent features [4], [5].